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Radio frequency fingerprinting techniques for device identification: a survey

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Abstract

The Internet of Things (IoT) paradigm and the advanced wireless technologies of 5G and beyond are expected to enable diverse applications such as autonomous driving, industrial automation, and smart cities. These applications bring together a vast and diverse IoT device population that occupy radio frequency spectrum. Such a large number of wireless devices expose previously unheard-of threat surfaces in addition to the bandwidth shortage and throughput issues. Device identification is crucial in such scenarios not only to authenticate and authorize nodes, but also to employ different network services. One of the promising solutions for device identification is the use of radio frequency (RF) fingerprinting. Recently, wireless device identification using RF fingerprinting along with machine learning and deep learning technologies showed outstanding results in the recent contemporary domains. This paper presents a systematic literature review of RF fingerprinting identification of wireless devices by presenting the results as a graphical and tabular representation of statistical data obtained. Only experimental research papers were considered of over 130 journals and international conference papers that have been classified and evaluated from the year 2010 till date. This survey focuses on exploring the commonly used RF fingerprinting approaches, feature extraction and filtration techniques, and classification algorithm used in the device identification. Finally, open issues and challenges along with future directions have presented which were discovered during the process of analyzing the literature.

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Acknowledgements

We would like to express our sincere gratitude to the UAE General Civil Aviation Authority for establishing the Aerospace Center of Excellence and conducting this research study.

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Authors and Affiliations

  1. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Sohail Abbas & Manar Abu Talib

  2. Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, UAE

    Qassim Nasir, Sally Idhis & Mariam Alaboudi

  3. American University of Sharjah, Sharjah, UAE

    Ali Mohamed

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  1. Sohail Abbas
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  3. Qassim Nasir
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Contributions

All authors contributed to the study conception and design. Research was supervised by Sohail Abbas, Manar Abu Talib, and Qassim Nasir. Data collection, material preparation, and analysis were performed by Sally Idhis, Mariam Alaboudi, and Ali Mohamed. Sohail Abbas contributed significantly in producing the final version of the manuscript. All authors read and approved the final manuscript.

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Appendix 1

Appendix 1

See Tables 8 and 9.

Table 8 Papers used in the SLR
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Table 9 QAR scores
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Abbas, S., Abu Talib, M., Nasir, Q. et al. Radio frequency fingerprinting techniques for device identification: a survey. Int. J. Inf. Secur. 23, 1389–1427 (2024). https://doi.org/10.1007/s10207-023-00801-z

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